Department of Biomedical Sciences, Center for Genome Research, University of Modena and Reggio Emilia, Modena, Italy.
Bioinformatics. 2011 Sep 1;27(17):2446-7. doi: 10.1093/bioinformatics/btr404. Epub 2011 Jul 7.
Chromosomal patterns of genomic signals represent molecular fingerprints that may reveal how the local structural organization of a genome impacts the functional control mechanisms. Thus, the integrative analysis of multiple sources of genomic data and information deepens the resolution and enhances the interpretation of stand-alone high-throughput data. In this note, we present PREDA (Position RElated Data Analysis), an R package for detecting regional variations in genomics data. PREDA identifies relevant chromosomal patterns in high-throughput data using a smoothing approach that accounts for distance and density variability of genomics features. Custom-designed data structures allow efficiently managing diverse signals in different genomes. A variety of smoothing functions and statistics empower flexible and robust workflows. The modularity of package design allows an easy deployment of custom analytical pipelines. Tabular and graphical representations facilitate downstream biological interpretation of results.
PREDA is available in Bioconductor and at http://www.xlab.unimo.it/PREDA.
Supplementary information is available at Bioinformatics online.
基因组信号的染色体模式代表了分子指纹,这些指纹可能揭示了基因组的局部结构组织如何影响功能控制机制。因此,对多种基因组数据和信息的综合分析可以提高分辨率并增强对独立的高通量数据的解释。在本说明中,我们介绍了 PREDA(位置相关数据分析),这是一个用于检测基因组数据中区域变化的 R 包。PREDA 使用一种平滑方法来识别高通量数据中的相关染色体模式,该方法考虑了基因组特征的距离和密度变化。定制的数据结构允许有效地管理不同基因组中的各种信号。各种平滑函数和统计信息可实现灵活而强大的工作流程。包设计的模块化允许轻松部署自定义分析管道。表格和图形表示形式有助于下游对结果进行生物学解释。
PREDA 可在 Bioconductor 中获得,并可在 http://www.xlab.unimo.it/PREDA 上获得。
补充信息可在 Bioinformatics 在线获得。